AI Threat AlertChronoState: Hidden Elapsed-Time Conditioning for Temporal-State Action Selection in Frozen-Backbone Language Models
does not establish autonomous time tracking, broad unseen-family abstraction, or superiority over prompt-injected timestamps
Imperceptible Jailbreaking against Large Language Models
imperceptible jailbreaks achieve high attack success rates against four aligned LLMs and generalize to prompt injection attacks, all without producing any visible modifications in the written prompt. Our code
Verifying Intent and Harm: A Unified Defense Against LLM-Generated Threats
conflict resolution. We formalize a threat model for prompt-response attacks and evaluate the framework across five threat categories: jailbreaks, prompt injection, phishing, cyber abuse, and harmful content. Experiments
BenchOverflow: Measuring Overflow in Large Language Models via Plain-Text Prompts
large language models (LLMs) in which plain-text prompts elicit excessive outputs, a phenomenon we term Overflow. Unlike jailbreaks or prompt injection, Overflow arises under ordinary interaction settings
ProjGuard: Safety Monitoring for Computer-Use Agents via Low-Dimensional Projections
real operating systems, but this capability has also increased the risks posed by prompt injection, indirect instructions, and visual attacks. Existing defenses typically rely on analyzing the prompt or each
ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems
Existing research on LLM agent security mainly focuses on prompt injection and unsafe input/output behaviors. However, as agents increasingly rely on third-party tools and MCP servers, a new class
Cascade: Composing Software-Hardware Attack Gadgets for Adversarial Threat Amplification in Compound AI Systems
with algorithmic weaknesses: (1) Exploiting a software code injection flaw along with a guardrail Rowhammer attack to inject an unaltered jailbreak prompt into an LLM, resulting in an AI safety
Kairos: Transactional Security Enforcement at the System Boundary for LLM Agents
injected instruction, and since the model does not reliably distinguish instructions from data, indirect prompt injection should be mitigated by mediating the effects the agent produces. Such mediation faces
GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
Prompt), a novel method that introduces instance-level adaptability to the prompting process. GAP-Prompt consists of three synergistic modules: (1) instance-conditioned gating, which dynamically determines optimal prompt injection
Protecting Context and Prompts: Deterministic Security for Non-Deterministic AI
Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot prevent. We introduce two novel primitives--authenticated prompts and authenticated context
Compromise Is Not Consequence: Evaluating Task-Scoped Authorization in LLM Agents with Paired Replay
cross-action and cross-resource consequences under researcher-supplied task authority, not prevention of prompt injection. A bounded six-task AgentDojo extension preserves native scoring and records 11/24 injected broad
Evaluation of Prompt Injection Defenses in Large Language Models
LLM-powered applications routinely embed secrets in system prompts, yet
PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts
making agent prompts valuable intellectual property. However, in untrusted deployments, adversaries can copy and reuse these prompts with other proprietary LLMs, causing economic losses. To protect these prompts, we identify
Efficient and Adaptable Detection of Malicious LLM Prompts via Bootstrap Aggregation
However, these systems remain susceptible to malicious prompts that induce unsafe or policy-violating behavior through harmful requests, jailbreak techniques, and prompt injection attacks. Existing defenses face fundamental limitations: black
VIGIL: Defending LLM Agents Against Tool Stream Injection via Verify-Before-Commit
agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated metadata and runtime feedback hijack execution flow. Existing defenses encounter
Right Tool, Wrong Parameters: Defending LLM Agents Against Parameter Poisoning
Indirect Prompt Injection (IPI) poses a critical threat to LLM agents. Existing defenses largely focus on whether an injection induces a new tool invocation or unintended action. In this paper
Unsupervised Features Mining via Activation Geometry
features. Finally, we use the same method to select the best training datasets for prompt-injection classifier probes: while similarity between ordinary activations is almost unrelated to downstream performance
Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems
instructions, process external data, invoke tools, and coordinate with other agents. These capabilities make prompt-injection and jailbreak attacks more consequential, especially as attackers adopt model-guided automation to scale
From Secure Agentic AI to Secure Agentic Web: Challenges, Threats, and Future Directions
Secure Agentic Web. We first summarize a component-aligned threat taxonomy covering prompt abuse, environment injection, memory attacks, toolchain abuse, model tampering, and agent network attacks. We then review defense
Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security
increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate outputs. Such attacks, including jailbreaking and prompt injection, pose significant risks